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Salesforce open model AI strategy aims for trust, automation

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Innhold levert av TechTarget Editorial. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av TechTarget Editorial eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

Salesforce was an early adopter of generative AI, seizing on large language model technology from OpenAI to integrate into its own applications.

But the CRM and CX giant quickly evolved an open model strategy. It now gives customers access to multiple third-party LLMs while providing its own AI trust layer to try to ensure that Salesforce users can safely rely on AI-generated outputs.

Jayesh Govindarajan, senior vice president at Salesforce AI, calls this approach "BYOLLLM," or bring your own LLLM.

"The Salesforce LLM strategy is to provide an open-model ecosystem for our customers," Govindarajan said on the Targeting AI podcast from TechTarget Editorial.

"Salesforce-developed models are, of course, available out of the box on the AI stack, but customers can also bring their own LLMs. And to support this level of choice and diversity, the trust layer is model-agnostic," he continued.

As befits its core customer base, Salesforce sees sales, marketing and customer service applications as most ripe for generative AI, and that is where the vendor is focusing on the technology as a productivity engine, Govindarajan said.

Similar conversations, whether taking place in email or other messaging formats, can be automated with generative AI so the technology is embedded in daily workflows.

An example Govindarajan cited is using generative AI to let a marketing person easily make a marketing campaign multilingual.

"How do we make a customer service person more efficient? How do we make a rock star salesperson 10 times more successful? How do we make a marketing manager create campaigns that convert really well?" Govindarajan said.

"It's not easy to do that. You want to do it with safety, security, and trust," he said. "As you know, the systems can go off. So, you want to have the right guardrails in place to be able to shape it into the right form."

Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 30 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems.

  continue reading

23 episoder

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iconDel
 
Manage episode 416926329 series 3493557
Innhold levert av TechTarget Editorial. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av TechTarget Editorial eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

Salesforce was an early adopter of generative AI, seizing on large language model technology from OpenAI to integrate into its own applications.

But the CRM and CX giant quickly evolved an open model strategy. It now gives customers access to multiple third-party LLMs while providing its own AI trust layer to try to ensure that Salesforce users can safely rely on AI-generated outputs.

Jayesh Govindarajan, senior vice president at Salesforce AI, calls this approach "BYOLLLM," or bring your own LLLM.

"The Salesforce LLM strategy is to provide an open-model ecosystem for our customers," Govindarajan said on the Targeting AI podcast from TechTarget Editorial.

"Salesforce-developed models are, of course, available out of the box on the AI stack, but customers can also bring their own LLMs. And to support this level of choice and diversity, the trust layer is model-agnostic," he continued.

As befits its core customer base, Salesforce sees sales, marketing and customer service applications as most ripe for generative AI, and that is where the vendor is focusing on the technology as a productivity engine, Govindarajan said.

Similar conversations, whether taking place in email or other messaging formats, can be automated with generative AI so the technology is embedded in daily workflows.

An example Govindarajan cited is using generative AI to let a marketing person easily make a marketing campaign multilingual.

"How do we make a customer service person more efficient? How do we make a rock star salesperson 10 times more successful? How do we make a marketing manager create campaigns that convert really well?" Govindarajan said.

"It's not easy to do that. You want to do it with safety, security, and trust," he said. "As you know, the systems can go off. So, you want to have the right guardrails in place to be able to shape it into the right form."

Shaun Sutner is senior news director for TechTarget Editorial's information management team, driving coverage of artificial intelligence, unified communications, analytics and data management technologies. He is a veteran journalist with more than 30 years of news experience. Esther Ajao is a TechTarget Editorial news writer and podcast host covering artificial intelligence software and systems.

  continue reading

23 episoder

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